import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
import tensorflow as tf
from joblib import load

class NN(object):
    def __init__(self):
        self.df = pd.read_csv('./NN/scenic_data.csv')
        self.loaded_model = tf.keras.models.load_model('NN/my_model.keras')
        self.scaler = load('NN/scaler.joblib')

    def get_hourly_trend(self):
        """获取预测结果
        """
        n_steps = 7
        latest_data = self.df.iloc[-n_steps:].values  # 取最后七天数据
        latest_data = latest_data.reshape(1, n_steps, latest_data.shape[1])

        # 预测与反归一化
        predicted = self.loaded_model.predict(latest_data)
        predicted_count = self.scaler.inverse_transform(predicted)  # 反归一化
        predicted_count[predicted_count < 0] = 0  # 防止负数

        hourly_trend = predicted_count[0].astype(int).tolist()
        print(hourly_trend)


if __name__ == '__main__':
    nn = NN()
    nn.get_hourly_trend()